audience-profile
This Claude Code skill generates detailed buyer personas by synthesizing product information, customer research, and behavioral data to map demographics, psychographics, jobs-to-be-done, objections, and content preferences. Use it when developing targeted marketing strategies, creating audience segments, or refining messaging for campaigns that require understanding customer motivations, pain points, and decision-making processes across B2B and B2C contexts.
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro /tmp/audience-profile && cp -r /tmp/audience-profile/skills/audience-profile ~/.claude/skills/audience-profileSKILL.md
# /digital-marketing-pro:audience-profile
## Purpose
Build a rich, actionable buyer persona that goes beyond basic demographics. Captures psychographic drivers, behavioral patterns, jobs-to-be-done, objections, and content consumption preferences to inform all marketing activities.
## Input Required
The user must provide (or will be prompted for):
- **Product/service**: What the brand offers
- **Customer type**: B2B buyer, B2C consumer, or both
- **Existing data**: Any customer research, survey data, analytics demographics, CRM data, or interview insights
- **Number of personas**: How many distinct personas to create (recommend 2-4)
- **Hypothesis**: Who the user thinks their ideal customer is (starting point)
## Process
1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. **Also check for guidelines** at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files. Check for custom templates at `~/.claude-marketing/brands/{slug}/templates/`. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Gather inputs through structured questions if data is limited
3. Build demographic profile: age range, role/title, company size (B2B), income range, geography, education
4. Develop psychographic profile: values, motivations, fears, aspirations, identity markers
5. Map behavioral patterns: where they spend time online, content formats preferred, purchase behavior, decision-making process
6. Define jobs-to-be-done: functional, emotional, and social jobs the product helps accomplish
7. Identify objections and barriers to purchase with counter-messaging
8. Document the buyer journey: trigger events, research process, evaluation criteria, decision influencers
9. Specify content preferences: platforms, formats, tone, topics they engage with
10. Give the persona a name and narrative summary for team alignment
## Output
A structured buyer persona document containing:
- Persona name and one-paragraph narrative
- Demographic snapshot
- Psychographic profile with motivations and values
- Jobs-to-be-done framework (functional, emotional, social)
- Day-in-the-life scenario
- Buyer journey map with touchpoints and decision criteria
- Objections and counter-messaging guide
- Content and channel preferences
- Messaging do's and don'ts for this persona
## Agents Used
- **marketing-strategist** — Persona development, JTBD framework, buyer journey mapping, audience segmentationDesign a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.
Generate 3-5 ad copy variations per platform — headlines, descriptions, and CTAs formatted to Google, Meta, LinkedIn, TikTok, X, and Pinterest specs — each scored 1-10 with policy-compliance flags, A/B testing groupings, and a message-match check against the landing page. Triggers on \"/digital-marketing-pro:ad-creative\", \"write ad copy for Meta\", \"give me RSA headline variations\", \"we need LinkedIn ad copy\", \"draft TikTok ad creative\". Reads the brand profile, guidelines, and compliance rules; routes video ad scripts to /digital-marketing-pro:video-script and gates AI-generated visuals for EU campaigns through /digital-marketing-pro:c2pa-metadata and /digital-marketing-pro:check.
Walk through adding a custom MCP server integration to the plugin — searches npm for an existing MCP package (or scaffolds a custom server from the plugin's guide), generates the exact .mcp.json entry, sets up environment-variable credentials, tests connectivity, and documents the tools the new server exposes. Triggers on \"/digital-marketing-pro:add-integration\", \"connect Ahrefs to the plugin\", \"add a new MCP server\", \"integrate our internal API\", \"hook up Stripe data\". Reads the brand profile and agency credential profiles at ~/.claude-marketing/credentials/ to map client-specific keys; custom builds follow skills/context-engine/custom-mcp-guide.md.
Audit how a brand appears across the 6 canonical AI answer surfaces — ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, Copilot — probing 10-25 queries into a numbered output bundle with per-platform visibility scorecards, citation-accuracy checks, a competitor matrix, content gaps, and an optimization playbook behind a four-gate quality scorecard. Triggers on \"/digital-marketing-pro:aeo-audit\", \"does ChatGPT know about our brand\", \"check our AI search visibility\", \"how does Perplexity describe us\", \"are we showing up in AI Overviews\". Reads the brand profile; reconciles probes against GSC actuals via /digital-marketing-pro:gsc-ai-performance and defines the AI-visibility scoring standard reused by geo-monitor and share-of-voice.
Strategy module for Answer Engine / Generative Engine Optimization — audits AI visibility, restructures content for citation, runs entity-consistency checks across Knowledge Graph, Wikidata, Wikipedia, Crunchbase, and LinkedIn, and produces JSON-LD schema specs, monitoring frameworks, and a 90-day LLM content strategy. Triggers on \"/digital-marketing-pro:aeo-geo\", \"how do we get cited by AI\", \"optimize for AI Overviews\", \"fix our entity consistency\", \"do we need llms.txt\". Reads the brand profile, compliance rules, and industry benchmarks; its measurement counterpart is /digital-marketing-pro:aeo-audit, with GSC actuals via /digital-marketing-pro:gsc-ai-performance.
Generate a portfolio-level dashboard across ALL client brands — per-client RAG health scores, campaign activity, budget pacing, aggregate KPIs, team utilization, pending approvals, upcoming deadlines, and an alerts panel — built for agency standups and weekly reviews. Triggers on \"/digital-marketing-pro:agency-dashboard\", \"how are all our clients doing\", \"portfolio health check\", \"budget pacing across accounts\", \"which accounts are at risk\". Enumerates every brand under ~/.claude-marketing/brands/ and pulls data via campaign-tracker.py, execution-tracker.py, and team-manager.py; drill into a single client with /digital-marketing-pro:performance-report or /digital-marketing-pro:client-report.
Invoke when the user needs to manage multiple client brands, view portfolio-level dashboards, generate client reports, manage SOPs, switch credential profiles, assign team tasks, configure regions, or generate executive summaries. Triggers on requests involving multi-client management, agency workflows, client onboarding, or portfolio oversight.
Invoke when the user needs help with marketing measurement, KPI definition, dashboard design, attribution reporting, performance analysis, competitive benchmarking, or translating data into marketing decisions. Triggers on requests involving metrics, reporting, analytics setup, or data interpretation.